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An Efficient Artificial Bee Colony Algorithm With an Improved Linkage Identification Method.

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Summary

This summary is machine-generated.

This study enhances the artificial colony (ABC) algorithm to overcome the curse of dimensionality. New strategies improve convergence speed and search thoroughness for complex optimization problems.

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Area of Science:

Artificial Intelligence
Optimization Algorithms
Computational Intelligence

Background:

The artificial colony (ABC) algorithm exhibits strong exploration but struggles with high-dimensional and nonseparable functions, leading to slow convergence.
Existing ABC variants face limitations due to the curse of dimensionality, hindering performance on complex optimization tasks.

Purpose of the Study:

To improve the performance of the artificial colony (ABC) algorithm, particularly on nonseparable functions and in high-dimensional spaces.
To address the convergence speed limitations of ABC by integrating enhanced updating mechanisms and linkage identification.

Main Methods:

An improved linkage identification strategy was embedded into ABC to differentiate between separable and nonseparable functions.
Novel strategies were introduced: population-based historical experience for exploration-exploitation balance, optimized scout bee initialization to reduce function evaluations, and multi-dimensional updates for poor performers.
The enhanced ABC algorithm was tested on benchmark functions (basic, CEC2010, CEC2013, CEC2014) and real-world problems.

Main Results:

The enhanced ABC algorithm demonstrated improved convergence speed and exploration capabilities on challenging benchmark functions.
The proposed strategies effectively addressed the curse of dimensionality, outperforming standard ABC on nonseparable and high-dimensional problems.
The multi-dimensional update strategy significantly aided convergence on nonseparable functions.

Conclusions:

The integrated linkage identification and novel updating strategies significantly enhance the artificial colony (ABC) algorithm's performance.
The proposed enhancements offer a robust solution for optimizing complex, high-dimensional, and nonseparable functions.
These novel concepts are applicable to other ABC variants and optimization algorithms.